Resource allocation

ABSTRACT

Embodiments of the disclosure provide a system and method of allocating a resource based on myriad input data. In some embodiments, the myriad input data include membership information, claims data, transactional data, etc. The myriad input data are sorted and organized in a meaningful association relationship before applied to a resource allocation modeling algorithm. The resource allocation modeling algorithm provides estimated resource necessary for the application chosen. For example, an insurance company may use membership information, claims data, transactional data, etc., to estimate how much reserves or funds it should hold to cover future claims within a certain timeframe.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a divisional of U.S. patent application Ser.No. 15/389,832, filed Dec. 23, 2016, issued as U.S. Pat. No. 10,937,102,which claims the benefit of U.S. Provisional Application No. 62/387,190,filed Dec. 23, 2015, all of which are incorporated by reference.

BACKGROUND

Certain institutions are required to have enough resources to cover anyincurred or future costs. Sometimes, the existence of these allocatedresources or funds is mandated by governing bodies, for example, countryand state governments. In some examples, financial institutions arerequired to have a certain amount of reserve money as a percentage ofdeposits in order to be able to cover daily withdrawals or emergencywithdrawals from their customers. In another example, insurancecompanies are required to hold enough reserve money to cover anyincurred claims.

The process of determining an amount of reserve money currently employedby insurance companies is conservative due to volatility in currentreserving models. Sometimes information needed to accurately predict therequired reserve amount is not received until months later. Currentmethodologies deal with this information lag by providing an incrediblemargin of safety that burdens institutions with requirements to hold alarge amount of capital on hand in the form of reserves money. Largeamount of capital on hand may adversely impact an institution's abilityto invest in the future.

BRIEF SUMMARY

Embodiments of the disclosure provide a system and method of allocatinga resource based on myriad input data. In some embodiments, the myriadinput data include membership information, claims data, transactionaldata, etc. The myriad input data are sorted and organized in ameaningful association relationship before being applied to a resourceallocation modeling algorithm. The resource allocation modelingalgorithm provides estimated resources necessary for the applicationchosen. For example, an insurance company may use membershipinformation, claims data, transactional data, etc., to estimate how muchreserve money or funds it should hold to cover future claims within acertain timeframe.

In one embodiment, a method for estimating reserves for an insurancecarrier using a data platform configured to collect data from one ormore source systems is provided. The method includes: collectingreserves relevant data from one or more data source systems over asystem defined time period; converting the reserves relevant data into areserves relevant data matrix, wherein the reserves relevant data matrixcomprises a plurality of features based on the reserves relevant datathat are organized based on the system defined time period; storing thereserves relevant data matrix at a reserves database of the dataplatform; executing a predictive model for each of the plurality offeatures of the reserves relevant data matrix to extrapolate a trend foreach individual feature; and combining the trend for each individualfeature to obtain a reserves estimate.

In another embodiment, a method for geographically allocating reservesfor an insurance carrier using a data platform configured to collectdata from one or more source systems is provided. The method includes:collecting reserves relevant data from one or more data source systemsfrom a plurality of geographic regions over a system defined timeperiod; converting the reserves relevant data into a reserves relevantdata matrix, wherein the reserves relevant data matrix comprises aplurality of features based on the reserves relevant data that areorganized based on the plurality of geographic regions and the systemdefined time period; storing the reserves relevant data matrix at areserves database of the data platform; executing a predictive model foreach of the plurality of features of the reserves relevant data matrixto extrapolate a trend for each individual feature within a geographicregion of the plurality of geographic regions; and combining the trendfor each individual feature within the geographic region to obtain areserves estimate for the geographic region.

In a further embodiment, a user interface for interacting with reservesrelevant data collected from reserves relevant data sources and beingutilized for estimating reserves for an insurance carrier is provided.The user interface includes a predictive variable interface configuredto display the reserves relevant data collected from the reservesrelevant data sources, wherein the predictive variable interfacedisplays the reserves relevant data over a selected time period. Theuser interface further includes a predictive model interface configuredto display, over the defined time period, a predictive model performanceand a predictive model variance for reserves estimates made based on thereserves relevant data over the selected time period.

In yet another embodiment, a non-transitory computer readable mediumcontaining computer executable instructions for estimating reserves foran insurance carrier using a data platform configured to collect datafrom one or more source systems is provided. The computer readableinstructions, when executed by a processor, cause the processor toperform steps including: collecting reserves relevant data from one ormore data source systems over a system defined time period; convertingthe reserves relevant data into a reserves relevant data matrix, whereinthe reserves relevant data matrix comprises a plurality of featuresbased on the reserves relevant data that are organized based on thesystem defined time period; storing the reserves relevant data matrix ata reserves database of the data platform; executing a predictive modelfor each of the plurality of features of the reserves relevant datamatrix to extrapolate a trend for each individual feature; and combiningthe trend for each individual feature to obtain a reserves estimate.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIGS. 1A and 1B provide an embodiment of a system schematic fordetermining cash reserves;

FIG. 2 provides an exemplary graphical comparison between differentmethods of predicting required reserves;

FIG. 3 provides a method of estimating reserves for a financialinstitution using the system of FIG. 1 ;

FIG. 4 provides an estimation of localized reserves modeling, accordingto an exemplary embodiment;

FIG. 5 provides a screen shot of a data visualization tool, according toan embodiment of the disclosure;

FIG. 6 provides another screen shot of the data visualization tool,according to an embodiment of the disclosure;

FIG. 7 provides another screen shot of the data visualization tool,according to an embodiment of the disclosure;

FIG. 8 provides another screen shot of the data visualization tool,according to an embodiment of the disclosure;

FIG. 9 provides another screen shot of the data visualization tool,according to an embodiment of the disclosure;

FIG. 10 provides another screen shot of the data visualization tool,according to an embodiment of the disclosure; and

FIG. 11 provides an electronic device according to an embodiment of thedisclosure.

DETAILED DESCRIPTION

Embodiments of the disclosure describe a system for determining reservesan institution should have on hand to cover various operationalchallenges. A goal of the provided system is to utilize recent datacombined with other data to increase granularity of information andapply a prediction methodology to the collected data to determine areserve amount. In some embodiments, this system may be used in themedical insurance field. The medical insurance field will be used as anexample in describing exemplary features of the system.

FIGS. 1A and 1B illustrate an embodiment of a reserves determinationsystem 100 in the context of a medical insurance provider. FIGS. 1A and1B illustrate an embodiment of a single system using two figures suchthat the system is more easily readable. For brevity sake, we will referfrom here on to FIG. 1 , which is collectively meant to refer to FIGS.1A and 1B as illustrating the reserves determination system 100.

In FIG. 1 , information flow is shown to progress from Source Systems104 to Data Platform 102 and then to Analytic Solutions 106. SourceSystems 104 may include, for example, information obtained from anElectronic Data Interchange (EDI), databases processing real timeeligibility (RTE), call centers like Automated Systems Design (ASD), aNavigator for collecting member events, claims database (Claims dB),enterprise data warehouse (EDW), a membership database (AMRS), and PlanDesign. Navigator is the institution's personal website where clientsmay make queries. Plan Design is a particular plan offered, which mayinclude information relating to copayments, out of pocket maximums,coverage in and out of network, cost of emergency room (ER) visits,premium services, etc.

The Source Systems 104 may provide data relating to claims data,transactional data, pre-certifications, eligibility request, membershipdata, prescription data, benefits data, ASD calls, episodes of care,weather, care management, provider contracts, lab results, etc. Claimsdata may include claims paid and incurred within a timeframe. Forexample, claims data may include paid and incurred claims in the currentmonth, claims paid and incurred one month ago, claims currently pending,and account payable (AP) held claims. AP held claims are claims heldwhile information related to the claim is being investigated. The numberof pre-certifications over a timeframe may be collected, for example,over the past 90 days. Pre-certification procedures are performed beforecertain activities to ensure a client or patient knows insurancecoverage for procedures, and the data obtained during thepre-certification process may allude to future costs the insuranceprovider should anticipate. The number of eligibility requests over aperiod of time, for example, 15 days, may be obtained. All of this datamay provide an indication of future claims, and, therefore, is usefulfor determining the amount of reserves the insurance provider shouldhold.

Membership data and benefits data may include age information, genderinformation, deductibles, out of pocket (OOP) maximums, co-insurance,etc. Prescription data and transaction data over a certain time periodmay provide insight to future costs. For example, number of newprescriptions in the past 28 days and the number of prescriptions in thepast 28 days may be collected. Patient expenditure on transactions likenon-prescription medication, other therapies, or over the counterdiagnoses or monitoring devices may also be classified undertransactional data collected. In certain aspects, information like beddays, that is, the number of days a patient is housed in a care facilityis a lead indicator for predicting future expenditure. In otherexamples, episodes of care data which provide average episodes of caredollars are used as input data to the system. Episodes of carecorrespond to a collection of claims that are grouped together forcertain conditions. For example, a heart attack or pregnancy may have acollection of claims for an episode of care. These episodes may bechronic or non-chronic as evidenced in the distinction between pregnancyand diabetes. In some instances, weather related information may becollected. These include road accident and ski accident data.

FIG. 1 shows example frequencies or time periods associated with thedifferent information sources under Source Systems 104. In addition tothe different time periods, some show different modalities of achievinginformation transfer to the Data Platform 102, for example, throughScript or Sqoop. In the exemplary embodiment of FIG. 1 , Sqoop is usedto input data into Apache Hadoop, and Script is used in general todepict customized scripts and modeling for the information flow.

Data Platform 102 provides an overview of the different modes oforganizing the disparate data collected from the Source Systems 104. EDIand databases storing RTE data provide eligibility data, for example, ona monthly basis and the information obtained is pooled as Eligibility108. Member events are provided by ASD and Navigator and processedthrough extracting, transforming, and loading (ETL) 110 the data toassociate member activity with date information. This associationcreates a reserves relevant data matrix that contains the data from theASD and Navigator systems organized as features and based on a systemdefined time period, such as the aforementioned date information. Memberevents may include phone calls, member online usage, etc. In someexamples, members may search for a doctor or a specialist online using acomputer or a phone, and this information may be an indicator of afuture expense/claim since a specific doctor is being sought after. Allof this data is then extracted, transformed and loaded by the ETL 110into a plurality of features organized over the system defined timeperiod into the reserves relevant data matrix.

Claims dB, EDW, and AMRS provide additional information about patientmedical information. For example, claims data, pre-certifications, beddays, episodes of care, prescription data, and membership informationmay be combined with patient activity information, associating featuresprovided with date or time information in another extracting,transforming, and loading (ETL) 112. As such, in the illustratedembodiment, the ETL 112 combines the reserves relevant data matrixcreated by ETL 110 with data from the Claims dB, EDW and AMRS SourceSystems 104. This combination creates another reserves relevant datamatrix containing the data from the various systems from Source Systems104 organized over the system defined time period. Similar to the abovedescription of ETL 110, the reserves relevant data matrix created by theETL 112 also organizes the data from the Source Systems (Claims dB, EDWand AMRS) into a plurality of features organized over the system definedtime period.

The system defined time period can be set by a user of the Data Platform102. Further, this time period can be changed based on a desiredcollection of data to analyze. For instance, in one embodiment, a usermay desire to collect and organize all reserves relevant data over amonth, while in another embodiment, the user may desire to obtain allreserves relevant data over the past 10 days. As such, the systemdefined time period is variable and can be set over any time perioddesired.

The Data Platform 104 in the illustrated embodiment of FIG. 1 includestwo ETLs 110 and 112. However, in other embodiments, more or fewer ETLsmay be utilized. For instance, a single ETL could collect and convertall of the reserves relevant data into a reserves relevant data matrixover the system defined time period. Additionally, several ETLs may bepresent, such as an ETL for each input from the Source Systems 104,which in turn may feed any number of other ETLs for a chain ofextracting, transforming, and loading of the data.

Returning to the illustrated embodiment, all of the Source Systems 104data collected thus far and the pre-sorting and pooling of the data isfurther combined and stored in a Reserves database 114. In someembodiments, the data stored in the Reserves database 114 is monthlydata, while in other embodiments the data may be over a period of days.The Reserves database 114 then serves as a repository to supply afeature matrix that when coupled with statistical algorithms like linearregression provides reserve estimates that may be stored in the ReserveEstimates database 118. In certain embodiments, instead of dealing withlarge databases, datamarts are used after combining collected data atdifferent stages and extracting important features most relevant toestimating the reserves.

The different databases in Data Platform 102 are shown as separatedatabases, but these may be a single or distributed database physicallyhoused at different locations. In some embodiments, Apache Hadoop isused for interfacing with Data Platform 102, therefore Apache Hiveinfrastructure is used for data summarization, query, and analysis. Insome embodiments, Spark with Scala may be used in addition to the Hadoopframework as shown in FIG. 1 where Eligibility 108 uses Spark and Scalafor increased speed due to in-memory processing of large amounts ofdata.

The Reserves Model 116 in FIG. 1 serves to analyze, clean up, andorganize data before storing the data in the Reserves Estimates database118 (or in some cases datamart). In some embodiments, the Reserves Model116 makes a prediction and stores the predicted results in the ReservesEstimates database 118. The Reserves Model 116 makes its predictionbased on reserves relevant data matrix from Reserves database 114 byapplying a data modeling function to the various features collected andorganized in the reserves relevant data matrix. In certain embodiments,this data modeling applies a predictive model that develops a trend andextrapolates that trend for each feature organized in the reservesrelevant data matrix. The various trends developed for each feature arethen combined to obtain the reserves estimate.

In a particular embodiment, each trend may be assigned a weighting valuesuch that combination with other weighted trends, by the Reserves Model116, affects the overall combination determining the reserves estimate.In this manner, certain features can affect the reserves estimate moreor less based on the assigned weight. The weight can be assigned perfeature either automatically by the Data Platform 102 or via user inputat a user interface at the Analytic Solutions 106. The weight can beapplied prior to determining the reserves estimate or post determinationwhen already stored in the Reserve Estimate database 118. For instance,in one exemplary embodiments, the trends utilized by the Reserves Model116 may be a cumulative claims paid two months ago assigned a weight of0.09069, a cumulative claims paid four months ago assigned a weight of−0.03864, a pending claims assigned a weight of 0.35105, claims waitingto be funded assigned a weight of 0.93229, claims waiting to be paidassigned a weight of 0.78372, eligibility requests assigned a weight of7.78668, and approved bed days assigned a weight of −760.86141.

Examples of various data/predictive modeling functions are a linearregression, a non-linear regression, a support vector machine, a neuralnetwork, a decision tree, a random forest, or a time series analysis.The previous list is not exclusive, as other data/predictive modelingfunctions may be contemplated. Further, the Reserves Model 116 may applyits data model at various time periods, as requested by a user, or on asystem defined/preset basis.

In some embodiments, the predictions include statistical reallocation ofreserves based on new information, thus providing a feedback systembetween the Reserves Model 116 and the Reserves Estimates database 118.In some embodiments, the Reserves Estimates database 118 is organized ona macro level, providing, for example, reserves needed for a certainterritory like a country, such as the whole United States. In otherembodiments, the Reserves Estimates are organized on a micro level,providing reserves needed for a certain region, like a state or localmunicipality. In other embodiments, a combination of regions orterritories, for example, a grouping of countries or states. These mayinclude reserve estimates for North America, Scandinavia, etc. orreserve estimates for the Northeast or Midwest, etc. In theseembodiments, the data collected from Source Systems 104 is done so basedon the desired region or regions. The ETL, such as ETLs 110 and 112 willfurther convert the data such that it is organized not just over thesystem defined time period but also per region/regions.

Additionally, in certain embodiments, the Reserves Estimates database118 may be organized at the individual level. In these embodiments, datafrom Source Systems 104 may be collected at the individual member levelin order to determine an amount of reserves apportioned to theindividual member.

In FIG. 1 , after the Data Platform 102, data flows from the ReservesEstimates database 118 towards Analytic Solutions 106. AnalyticSolutions 106 comprises modes of using or displaying informationcontained in the Reserves Estimates database 118 (embodiments of whichare illustrated in FIGS. 5-10 ). For example, Analytic Solutions 106 maydisplay estimated reserves data in Tableau or other data visualizationproducts. In other embodiments, Analytic Solutions 106 may includecreating Reserve Reports in Microsoft Excel or other programs. In someembodiments, a user of the report is able to use new market data toupdate information in the Reserves Estimates database 118. For instance,new market data useful for updating information in the ReservesEstimates database 118 would be a change in deductible amounts,eligibility requests and other such data relevant to a certain market,such as healthcare.

Embodiments of the system thus provided create models at macro, micro,and even individual member levels. The models may be used to predict notjust monthly but even daily reserves an institution may be required tohold for dealing with individual transactions. In some embodiments, thedifferent reserves at different levels require different models in theReserves Model of FIG. 1 . In some embodiments, the reserves for marketsectors may be important as well in order to figure out reservebreakdown per sector/region.

Embodiments of the system may be used not just in the medical insurancefield, but may be applied for pricing services. In other areas, thesystem may be used to detect an emerging epidemic in a geographiclocation based on several future indicators.

An exemplary embodiment that demonstrates how the system of FIG. 1 maybe used by a medical insurance company will now be discussed. Theinsurance company may collect data pertaining to pre-certification,eligibility requests, episodes of care, claims, membership benefits, ASDcalls, and prescription data. The insurance company receives thisinformation from various sources, for example, through an informationexchange and through direct interaction with health care providers orindirectly through patients or users on the insurance company's website.

The collected data is aggregated and stored in granular fashion. Thismeans that the data, although aggregated, may be associated with andgrouped at an individual member level. After collecting and organizingthe data, certain algorithms, such as linear regression and/or otheralgorithms may be applied to the data. In some instances, the insurancecompany is able to rank the importance of each collected data or qualityof the data based on the age of the data. For example, the insurancecompany may place more importance on data gathered two months agorelative to data gathered a year ago due to, for example, fluctuation inhealthcare prices. The linear regression applied to the data providesinformation regarding the reserves required for the current model. Thisreserves information is stored in the Reserves Estimates database 118 ofFIG. 1 .

FIG. 2 illustrates an example visualizing a reserves result when usingthe system of FIG. 1 to predict reserves required. The medical insurancecompany is able to generate data on a monthly basis, and applyingdifferent algorithms, the system of FIG. 1 is shown to provide a betterestimate of actual claims and is shown to have lower variability thanexisting methods. The system of FIG. 1 uses a Data Science (DS) Modelwhich is labeled as “1.” The claims data is starred and as can be shown,the other methods, labeled as “2” and “3” do not track the claims dataas well as the system of FIG. 1 . The mean absolute error is the leastfor “1,” and the standard deviation is the lowest as well.

FIG. 3 illustrates a method of estimating reserves 300 for a financialinstitution using the reserves determination system 100 of FIG. 1 . Atstep 302, the Data Platform 102 collects the reserves relevant data fromthe Source Systems 104. At step 304, the Data Platform 102 converts thereserves relevant data into a plurality of features organized over asystem defined time period into a reserves relevant data matrix. At step306, the Data Platform 102 stores the reserves relevant data matrix atthe Reserves database 114. At step 308, the Reserves Model 116 executesa data/predictive model for each feature of the reserves relevant datamatrix to obtain a data trend for each feature. At step 310, theReserves Model 116 combines each extrapolated trend for each feature toobtain a reserves estimate over the system defined time period. Asdiscussed above, in certain embodiments, at step 310, the Reserves Model116 also may apply a weighting factor for each trend prior to combiningwith other weighted trends. At step 312, the reserves estimate is storedin the Reserves Estimate database 118.

At step 314, the Data Platform 102 (see FIG. 1 ) determines whetherupdated reserves and/or market data has been received. Updated reservesdata would be additional reserves relevant data from the Source Systems104 being utilized to supplement the reserves estimate stored in theReserves Estimate database 118. Updated market data is received from theAnalytic Solutions 106 and may be utilized to update the reservesestimate based on the specific market data. For instance, an example ofrelevant market data may be a proportion of members in the relevantmarket, a historical claims volume in the relevant market or the typesof insurance plans offered. If the Data Platform 102 determines that noupdated reserves and/or market data has been received, then the DataPlatform 102 does nothing, at step 316. If the Data Platform 102determines that updated reserves and/or market data has been received,then, at step 318, the Reserves Model 116 updates the reserves estimatebased on the updated reserves and/or market data. At step 320, theupdated reserves estimate is stored at the Reserves Estimate database118.

FIG. 4 illustrates an estimation of localized reserves modeling,according to an exemplary embodiment. FIG. 4 shows three charts, oneillustrating a mean error comparison between two data models per variouslocal markets/geographic regions. Another chart illustrates a max errorcomparison between the two data models per the same local markets. Thethird chart illustrates the cumulative membership of the financialinstitution (such as an insurance company) in the various local markets.These charts would be useful in determining a combination of variousdata types from the Source Systems 104 (see FIG. 1 ) and the type ofpredictive model utilized to give the best results in the reservesestimate. The percent error determination is determined by comparingactual reserves data from the past against a prediction made over thesame time period.

Turning now to FIGS. 5-10 , various embodiments of the AnalyticSolutions 106 (see FIG. 1 ) are illustrated. Each of these figuresrepresents an embodiment of a user interface/data analysis tool embodiedby Analytic Solutions 106. Utilizing Analytic Solutions 106, a user isable to review reserves estimate data from the Data Platform 102 andselect and/or update the various models applied by the Reserves Model116 and collected data from the Source Systems 104. Further, the usermay also provide updated market data to the Data Platform 102 as well asupdate any system defined time period over which data is collected inorder to make a reserves estimate.

The Analytic Solutions 106 illustrated in FIG. 5 provides a comparisonof three models: Pends, Completion Factor and Data Science. For eachmodel, three curves are depicted in the charts on the left. The curvelabeled “1” is an unadjusted restated reserves over a specified timeperiod. The curve labeled “2” is an adjusted restated reserves over thespecified time period. The curve labeled “3” is a comparison of one ofthe three models over the specified time period. The charts on the rightside of the illustration represent the percent error between theadjusted restated reserves and the model. As can be seen, the DataScience model provides the least error between the adjusted restatedreserves and the model prediction.

The Analytic Solutions 106 illustrated in FIG. 6 provides charts ofvarious factors organized in the reserves relevant data matrix over thesystem defined time period. Each chart includes two curves: the curvelabeled “1” illustrates actual data from the Source Systems 104; and thecurve labeled “2” illustrates the predicted trend for that particulardata from the Source Systems 104 that is used to formulate the reservesestimate. This embodiment of the Analytic Solutions 106 allows a user toview specific factor based data points utilized in the predictivemodels.

The Analytic Solutions 106 illustrated in FIG. 7 provides a collectionof charts illustrating the most relevant factors/data collected from theSource Systems 104 (see FIG. 1 ). As illustrated, each chart shows acomparison between the data collected from the Source Systems 104 thatis most relevant to predicting the restated reserves. Each chart shows acurve “1” that shows the restated reserves, and a second curve labeled“2” that shows data collected from the Source Systems 104. Utilizing theAnalytic Solutions 106 shown in FIG. 7 , a user is able to review themost relevant data collected from the Source Systems 104. This data mayallow the user to change and update various types of data to provide tothe predictive models.

The Analytic Solutions 106 illustrated in FIG. 8 provides a comparisonof claims data collected from Source Systems 104. FIG. 8 shows twocharts. The top chart illustrates a number of paid claims over a periodof months. The bottom chart illustrates an amount of dollarsappropriated for paid claims, pended claims, wait pay, and wait fundover a period of months.

The Analytic Solutions 106 illustrated in FIG. 9 provides three chartsshowing medical utilization. The chart on the top-left portion of FIG. 9illustrates a percentage of members that have reached their deductible.Each curve illustrates the percentage of members to reach theirdeductible over a period of months during a certain calendar year—2014and 2015, as illustrated. The chart on the top-right of FIG. 9illustrates a cumulative number of bed days over a period of 50 days.The chart at the bottom of FIG. 9 illustrates an amount of paid claimdollars for three places of claim service: (1) outpatient, (2)inpatient, and (3) emergency. Each of these charts illustrates a type ofdata collected from Source Systems 104 and organized into features overa system defined time period in the reserves relevant data matrix, asdiscussed in relation to FIG. 1 .

The Analytic Solutions 106 illustrated in FIG. 10 provides a singlechart that shows a predicted monthly change in reserves estimated usingthe previously discussed Data Sciences model. The chart shows aplurality of geographic locations and a shading that reflects apercentage change in reserves for the month for that specific geographiclocation. This chart is useful for determining how the reserves estimateis predicting an allocation of reserves for a plurality of localgeographic regions.

FIG. 11 illustrates an electronic device 1100 according to an embodimentof the disclosure. Electronic devices, for example, servers andterminals comprising the Source Systems 104, the Data Platform 102 andthe Analytic Solutions 106, in certain embodiments, may be computerdevices as shown in FIG. 11 . The device 1100 may include one or moreprocessors 1102, memory 1104, network interfaces 1106, power source1108, output devices 1110, input devices 1112, and storage devices 1114.Although not explicitly shown in FIG. 11 , each component provided isinterconnected physically, communicatively, and/or operatively forinter-component communications in order to realize functionalityascribed to the various entities identified in FIG. 11 . To simplify thediscussion, the singular form will be used for all components identifiedin FIG. 11 when appropriate, but the use of the singular does not limitthe discussion to only one of each component. For example, multipleprocessors may implement functionality attributed to processor 1102.

Processor 1102 is configured to implement functions and/or processinstructions for execution within device 1100. For example, processor1102 executes instructions stored in memory 1104 or instructions storedon a storage device 1114. In certain embodiments, instructions stored onstorage device 1114 are transferred to memory 1104 for execution atprocessor 1102. Memory 1104, which may be a non-transient,computer-readable storage medium, is configured to store informationwithin device 1100 during operation. In some embodiments, memory 1104includes a temporary memory that does not retain information stored whenthe device 1100 is turned off. Examples of such temporary memory includevolatile memories such as random access memories (RAM), dynamic randomaccess memories (DRAM), and static random access memories (SRAM). Memory1104 also maintains program instructions for execution by the processor1102 and serves as a conduit for other storage devices (internal orexternal) coupled to device 1100 to gain access to processor 1102.

Storage device 1114 includes one or more non-transient computer-readablestorage media. Storage device 1114 is provided to store larger amountsof information than memory 1104, and in some instances, configured forlong-term storage of information. In some embodiments, the storagedevice 1114 includes non-volatile storage elements. Non-limitingexamples of non-volatile storage elements include floppy discs, flashmemories, magnetic hard discs, optical discs, solid state drives, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories.

Network interfaces 1106 are used to communicate with external devicesand/or servers. The device 1100 may comprise multiple network interfaces1106 to facilitate communication via multiple types of networks. Networkinterfaces 1106 may comprise network interface cards, such as Ethernetcards, optical transceivers, radio frequency transceivers, or any othertype of device that can send and receive information. Non-limitingexamples of network interfaces 1106 include radios compatible withseveral Wi-Fi standards, 3G, 4G, Long-Term Evolution (LTE), Bluetooth®,etc.

Power source 1108 provides power to device 1100. For example, device1100 may be battery powered through rechargeable or non-rechargeablebatteries utilizing nickel-cadmium or other suitable material. Powersource 1108 may include a regulator for regulating power from the powergrid in the case of a device plugged into a wall outlet, and in somedevices, power source 1108 may utilize energy scavenging of ubiquitousradio frequency (RF) signals to provide power to device 1100.

Device 1100 may also be equipped with one or more output devices 1110.Output device 1110 is configured to provide output to a user usingtactile, audio, and/or video information. Examples of output device 1110may include a display (cathode ray tube (CRT) display, liquid crystaldisplay (LCD) display, LCD/light emitting diode (LED) display, organicLED display, etc.), a sound card, a video graphics adapter card,speakers, magnetics, or any other type of device that may generate anoutput intelligible to a user.

Device 1100 is equipped with one or more input devices 1112. Inputdevices 1112 are configured to receive input from a user or theenvironment where device 1100 resides. In certain instances, inputdevices 1112 include devices that provide interaction with theenvironment through tactile, audio, and/or video feedback. These mayinclude a presence-sensitive screen or a touch-sensitive screen, amouse, a keyboard, a video camera, microphone, a voice responsivesystem, or any other type of input device.

The hardware components described thus far for device 1100 arefunctionally and communicatively coupled to achieve certain behaviors.In some embodiments, these behaviors are controlled by software runningon an operating system of device 1100.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

The use of the terms “a” and “an” and “the” and “at least one” andsimilar referents in the context of describing the invention (especiallyin the context of the following claims) are to be construed to coverboth the singular and the plural, unless otherwise indicated herein orclearly contradicted by context. The use of the term “at least one”followed by a list of one or more items (for example, “at least one of Aand B”) is to be construed to mean one item selected from the listeditems (A or B) or any combination of two or more of the listed items (Aand B), unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the inventionand does not pose a limitation on the scope of the invention unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe invention.

Preferred embodiments of this invention are described herein, includingthe best mode known to the inventors for carrying out the invention.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the invention to be practicedotherwise than as specifically described herein. Accordingly, thisinvention includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the invention unlessotherwise indicated herein or otherwise clearly contradicted by context.

The invention claimed is:
 1. A user interface for interacting withreserves relevant data collected from reserves relevant data sources andbeing utilized for making reserves estimates for an insurance carrier byapplying a predictive model to the reserves relevant data, the userinterface comprising: a predictive variable interface configured to:convert, by an extract, transform, and load (ETL) system, the reservesrelevant data collected from the reserves relevant data sources over aspecified time period into a first reserves relevant data matrix and asecond reserves relevant data matrix, wherein the first reservesrelevant data matrix includes individual member eligibility datacombined with member claims-related data and the second reservesrelevant data matrix includes data relevant to member interactions withthe insurance carrier, and wherein the first and second reservesrelevant data matrices comprise a plurality of features based on thereserves relevant data; combine the first reserves relevant data matrixwith the second reserves relevant data matrix to form a third reservesrelevant data matrix, wherein the third reserves relevant data matrixcomprise the plurality of features associated with individual membersand based on a system defined time period and geographic locationsdescribed by associated geographic information; and display a graph foreach respective feature of the plurality of features from the reservesrelevant data matrix, wherein each graph comprises a first curve and asecond curve, the first curve plots data collected for the respectivefeature from the reserves relevant data sources, and the second curveplots a predicted trend for the respective feature; and a predictivemodel interface configured to display, over the specified time period, apredictive model performance and a predictive model variance for thereserves estimates made based on an application of the predictive modelto the reserves relevant data over the specified time period, whereinthe predictive model applies a modeling algorithm to the plurality offeatures of the third reserves relevant data matrix.
 2. The userinterface of claim 1, wherein the reserves relevant data comprises aplurality of data types and the user interface further comprises aleading indicators interface configured to display each of the pluralityof data types against reserves data over the specified time period. 3.The user interface of claim 2, wherein the plurality of data typescomprises claim data over the specified time period.
 4. The userinterface of claim 2, wherein the plurality of data types comprisesmember data over the specified time period, wherein the member datacomprises date information for each of a plurality of member data pointsof the member data.
 5. The user interface of claim 4, wherein a formatof the member data is converted by the user interface to member-by-datedata, wherein the member-by-date data contains the plurality of memberdata points associated with the date information.
 6. The user interfaceof claim 2, wherein the plurality of data types comprises eligibilitydata over the specified time period.
 7. The user interface of claim 1,wherein the predictive model interface is further configured to displaythe reserves estimates made based on the reserves relevant data over thespecified time period.
 8. The user interface of claim 2, wherein theplurality of data types comprises pre-certification data over thespecified time period.
 9. The user interface of claim 2, wherein theplurality of data types comprises prescription data over the specifiedtime period.
 10. The user interface of claim 2, wherein the plurality ofdata types comprises one or more of weather data, insurance carriernavigator data, and insurance carrier call log data.
 11. The userinterface of claim 1, wherein the predictive model is one or more of: alinear regression, a non-linear regression, a support vector machine, aneural network, a decision tree, a random forest, or a time seriesanalysis.
 12. A non-transitory computer readable medium storinginstructions for configuring a computer as a user interface comprising apredictive variable interface and a predictive model interface, whereinthe user interface interacts with reserves relevant data collected fromreserves relevant data sources, and the reserves relevant data isutilized for making reserves estimates for an insurance carrier byapplying a predictive model to the reserves relevant data, wherein, whenthe computer executes the instructions, the computer is configured to:convert, by an extract, transform, and load (ETL) system, the reservesrelevant data collected from the reserves relevant data sources over aspecified time period into a first reserves relevant data matrix and asecond reserves relevant data matrix, wherein the first reservesrelevant data matrix includes individual member eligibility datacombined with member claims-related data and the second reservesrelevant data matrix includes data relevant to member interactions withthe insurance carrier, and wherein the first and second reservesrelevant data matrices comprise a plurality of features based on thereserves relevant data; combine the first reserves relevant data matrixwith the second reserves relevant data matrix to form a third reservesrelevant data matrix, wherein the third reserves relevant data matrixcomprise the plurality of features associated with individual membersand based on a system defined time period and geographic locationsdescribed by associated geographic information; and display a graph foreach respective feature of the plurality of features from the reservesrelevant data matrix, wherein each graph comprises a first curve and asecond curve, the first curve plots data collected for the respectivefeature from the reserves relevant data sources, and the second curveplots a predicted trend for the respective feature; and display, overthe specified time period, a predictive model performance and apredictive model variance for the reserves estimates made based on anapplication of the predictive model to the reserves relevant data overthe specified time period, wherein the predictive model applies amodeling algorithm to the plurality of features of the third reservesrelevant data matrix.
 13. The non-transitory computer readable medium ofclaim 12, wherein the reserves relevant data comprises a plurality ofdata types, and wherein the non-transitory computer readable mediumstores further instructions that, when executed by the computer, furtherconfigure the computer to display each of the plurality of data typesagainst reserves data over the specified time period.
 14. Thenon-transitory computer readable medium of claim 13, wherein theplurality of data types comprises claim data over the specified timeperiod.
 15. The non-transitory computer readable medium of claim 13,wherein the plurality of data types comprises member data over thespecified time period, wherein the member data comprises dateinformation for each of a plurality of member data points of the memberdata.
 16. The non-transitory computer readable medium of claim 15,wherein a format of the member data is converted by the user interfaceto member-by-date data, wherein the member-by-date data contains theplurality of member data points associated with the date information.17. The non-transitory computer readable medium of claim 13, wherein theplurality of data types comprises eligibility data over the specifiedtime period.
 18. The non-transitory computer readable medium of claim12, wherein the predictive model interface is further configured todisplay the reserves estimates made based on the reserves relevant dataover the specified time period.
 19. The non-transitory computer readablemedium of claim 13, wherein the plurality of data types comprisespre-certification data over the specified time period.
 20. A userinterface for interacting with reserves relevant data comprising aplurality of data types collected from reserves relevant data sourcesand being utilized for making reserves estimates for an insurancecarrier by applying a predictive model to the reserves relevant data,the user interface comprising: a predictive variable interfaceconfigured to: convert, by an extract, transform, and load (ETL) system,the reserves relevant data collected from the reserves relevant datasources over a specified time period into a first reserves relevant datamatrix and a second reserves relevant data matrix, wherein the firstreserves relevant data matrix includes individual member eligibilitydata combined with member claims-related data and the second reservesrelevant data matrix includes data relevant to member interactions withthe insurance carrier, and wherein the first and second reservesrelevant data matrices comprise a plurality of features based on thereserves relevant data; combine the first reserves relevant data matrixwith the second reserves relevant data matrix to form a third reservesrelevant data matrix, wherein the third reserves relevant data matrixcomprise the plurality of features associated with individual membersand based on a system defined time period and geographic locationsdescribed by associated geographic information; and display a graph foreach respective feature of the plurality of features from the reservesrelevant data matrix, wherein each graph comprises a first curve and asecond curve, the first curve plots data collected for the respectivefeature from the reserves relevant data sources, and the second curveplots a predicted trend for the respective feature; and a predictivemodel interface configured to display the reserves estimates, apredictive model performance for the reserves estimates, and apredictive model variance for the reserves estimates all over thespecified time period, wherein the predictive model applies a modelingalgorithm to the plurality of features of the third reserves relevantdata matrix; and a leading indicators interface configured to displayeach of the plurality of data types against reserves data over thespecified time period, wherein the plurality of data types compriseclaim data, member data, eligibility data, pre-certification data, andprescription data all over the specified time period, wherein the memberdata comprises date information for each of a plurality of member datapoints of the member data, a format of the member data is converted bythe user interface to member-by-date data, and the member-by-date datacontains the plurality of member data points associated with the dateinformation, and wherein the predictive model is one or more of: alinear regression, a non-linear regression, a support vector machine, aneural network, a decision tree, a random forest, or a time seriesanalysis.